






"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

snake_case : Optional[int]     		=		"""facebook/wmt19-en-de"""
snake_case : List[Any]     		=		FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case : List[str]     		=		FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

snake_case : Any     		=		FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
snake_case : Optional[int]     		=		tokenizer(["""Making tiny model"""], return_tensors="""pt""")
snake_case : Dict     		=		tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
snake_case : Any     		=		"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de







"""simple docstring"""
def 							A						(				__snake_case: str  )    ->		list:

       """simple docstring"""





       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__snake_case  )  )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
 __import__("""doctest""").testmod()